Resource Management in RIS-Assisted Rate Splitting Multiple Access for Next Generation (xG) Wireless Communications: Models, State-of-the-Art, and Future Directions

TL;DR

The paper addresses the need for higher data rates and robust reliability in xG networks by examining the combined use of Reconfigurable Intelligent Surfaces (RIS) and Rate-Splitting Multiple Access (RSMA). It surveys over 60 studies across more than 20 system topologies, detailing reflective, transmissive, and STAR RIS implementations and categorizing downlink/uplink resource allocation approaches using optimization and ML techniques. Key contributions include a comprehensive taxonomy of RIS/R SMA models, a synthesis of capacity/outage analyses, and guidance on optimization methods (e.g., SCA, MM, BCD) and performance trends, highlighting when RIS-assisted RSMA outperforms traditional schemes. The work identifies open challenges—such as physically accurate RIS modeling, multi-antenna RIS-RSMA, and reduced reliance on SIC—and outlines future directions integrating ISAC, holographic RIS, and large-scale analyses, which are crucial for practical deployment and standardization.

Abstract

Next generation wireless networks require more stringent performance levels. New technologies such as Reconfigurable intelligent surfaces (RISs) and rate-splitting multiple access (RSMA) are candidates for meeting some of the performance requirements, including higher user rates at reduced costs. RSMA provides a new way of mixing the messages of multiple users, and the RIS provides a controllable wireless environment. This paper provides a comprehensive survey on the various aspects of the synergy between reconfigurable intelligent surfaces (RISs) and rate splitting multiple access (RSMA) for next-generation (xG) wireless communications systems. In particular, the paper studies more than 60 articles considering over 20 different system models where the RIS-aided RSMA system shows performance advantage (in terms of sum-rate or outage probability) over traditional RSMA models. These models include reflective RIS, simultaneously transmitting and reflecting surfaces (STAR-RIS), as well as transmissive surfaces. The state-of-the-art resource management methods for RIS-assisted RSMA communications employ traditional optimization techniques and/or machine learning techniques. We outline major research challenges and multiple future research directions.

Figures (17)

• Figure 1: A conceptual view of , , and - for a 2-user system. For and , an ellipse denotes a step.
• Figure 2: A conceptual view of , , -, and - for a 4-user system. For and , an ellipse denotes a step.
• Figure 3: System diagram of a K-user system implementing -.
• Figure 4: System diagram of a K-user system implementing -. A single group is shown for simplicity.
• Figure 5: A unit-cell of a reflective surface with DC biasing, and binary control through a single diode. Adapted from Yang2016.